Learning of and on manifolds in medical imaging (IN2107)
Considering the manifold of medical imaging data, i.e. the underlying topological space, facilitates the analysis, interpretation, and visualization of the data. This seminar focuses on machine and deep learning methods that either learn the manifold from high-dimensional data or use manifold-valued data as input. Selected material of methods and applications from the field of medical imaging will be covered. Basic problem formulations to recent advances will be discussed. This includes, but is not limited to:
- Introduction to manifolds
- Difference between learning on and of a manifold
- Examples of manifold-valued data in medical imaging
- State-of-the-art methods for manifold-valued data
- Clinical applications
Please register to: https://matching.in.tum.de/m/jz0zflh/q/6wi1lmq4yx
Check the intro slides here:
Julia A. Schnabel
Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.